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Smart lead routing automation built with Make.com routes incoming leads by score and automatically notifies the right Slack channel, removing manual sorting.
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My recent client is an Australian printing firm.
They already had clients coming in.
The problem was what happened after the agreement.
When a client agrees to payment terms, every delay matters.
A payment process that sits waiting for someone to follow up can slow down the entire client journey. And when clients experience unnecessary delays, it can affect more than cash flow. It can affect their confidence in the business.
So I built an Agreed Payment System in Make.com designed around the client journey.
The goal wasn’t simply to automate emails.
It was to create a process that keeps agreed payments moving, keeps communication consistent, and reduces the amount of manual follow-up required from the team.
The workflow takes the agreed payment process and routes it through the appropriate steps, conditions, and Gmail communications.
The system helps keep the process moving.
And this is where my background in sales development and client retention comes into the automation work.
I don’t look at automation as:
Trigger → Action → Done.
I look at it as:
Lead → Sale → Client → Experience → Retention → Repeat business.
Because getting a client is only part of the job.
You also need systems that make it easy for that client to keep doing business with you.
Sales development + client retention + workflow automation.
That’s the intersection I build around.
The handoff after a client agrees to terms is where many service businesses leak cash flow. Automating follow-up there is a practical B2B wedge because it ties the workflow directly to revenue.
AI-Agent Reporting Automation
Production AI-agent service that turns stakeholder requests into verified BI reports end to end: a request posted in Basecamp triggers a gated agent workflow that validates SQL against approved business logic, creates or updates the Metabase report, and verifies every write by exact read-back before replying with evidence. Containerized Node.js services (Docker Compose) with a fully offline test suite and CI. Built to cut the analytics team's ad-hoc reporting load - and to prove AI agents can be trusted with production writes when properly gated.
Solid setup. One thing that has saved me a lot in write-capable agents: log every agent turn (prompt, tool calls, outcome) to an audit table and add a replay mode — when a report comes out wrong, you can reproduce the exact run instead of debugging blind. Also worth setting a...
The idea
After my first build (the initial "Lead Follow-Up Automation," which was really just me learning the ropes), I wanted to prove I could build something that actually solved a real, painful problem for a real type of business not just a toy demo. Personal injury law firms are notorious for this: someone fills out a contact form after a car accident or injury, and if the firm doesn't get back to them within minutes, they just call the next firm on Google. Speed-to-lead is everything in that industry, and most small firms are still doing it manually, an intake person checking a shared inbox a few times a day. So I built Langford & Reyes as a mock client to show exactly how AI automation solves that gap.
The build
The pipeline was: a prospect fills out a Tally form → Make.com picks it up and routes it → Groq (running llama-3.3-70b-versatile) reads the intake details and qualifies the lead. Is this a real case, what's the injury, how urgent → Airtable logs everything as a structured record → Gmail fires off a response, formatted properly, to both the lead and the firm.
The whole point was compressing what used to take hours (or a full day) down to under 5 minutes from form submission to a qualified, routed lead sitting in the firm's inbox.
The challenges
This is where it got real.
Tally field mapping getting the form fields to actually map cleanly into Make.com without breaking when someone left a field blank or answered in an unexpected way.
Airtable 422 errors, classic "your data doesn't match what the API expects" pain, which meant going back and forth debugging exactly what Airtable's schema wanted versus what Make was sending.
Invalid JSON from multi-line prompts. Groq would sometimes return malformed JSON when the AI's response included line breaks or multi-paragraph reasoning, which broke the next step in the chain. That's a subtle one a lot of people don't anticipate until it happens to them.
Gmail formatting, getting emails to actually look like emails (not a wall of unformatted text) by manually inserting HTML <br> tags since Gmail doesn't auto-convert line breaks in API-sent messages.
The lesson
None of these are exotic problems they're the unglamorous plumbing issues that separate "I watched a YouTube tutorial and made a demo" from "I built something a real business could actually run on." That's really the value of Langford & Reyes as a case study: it's proof I've hit the annoying edge cases and know how to debug an AI pipeline end-to-end, not just wire together a happy path.
